Introducing principal component analysis
When building an ML model, the dataset that’s used to train the model may have redundant information in the predictors. The redundancy in the predictors/columns of the dataset arises from correlated features in the dataset and needs to be taken care of when using a certain class of models. In such cases, PCA is a popular technique to address such challenges as it reduces the feature dimension of the dataset and thus shrinks the redundancy. The problem of collinearity, which says that two or more predictors are linearly correlated in a model, could thus be relieved via dimension reduction using PCA.
Collinearity among the predictors is often considered a big problem when building an ML model. Using the Pearson correlation coefficient, it is a number between -1 and 1, where a coefficient near 0 indicates two variables are linearly independent, and a coefficient near -1 or 1 indicates that two variables are linearly related.
When two...
